SOTAVerified

Multi-Armed Bandits

Multi-armed bandits refer to a task where a fixed amount of resources must be allocated between competing resources that maximizes expected gain. Typically these problems involve an exploration/exploitation trade-off.

( Image credit: Microsoft Research )

Papers

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Showing 731–740 of 1262 papers

TitleStatusHype
Statistical Inference with M-Estimators on Adaptively Collected Data—0
Online certification of preference-based fairness for personalized recommender systems—0
Off-Policy Risk Assessment in Contextual Bandits—0
Censored Semi-Bandits for Resource Allocation—0
An Efficient Algorithm for Deep Stochastic Contextual Bandits—0
Leveraging Good Representations in Linear Contextual Bandits—0
Multinomial Logit Contextual Bandits: Provable Optimality and Practicality—0
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information—0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Deep Contextual Bandits for Fast Neighbor-Aided Initial Access in mmWave Cell-Free Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NeuralLinear FullPosterior-MRCumulative regret1.92—Unverified
2Linear FullPosterior-MRCumulative regret1.82—Unverified